Viral co-detection of influenza virus and other respiratory viruses in hospitalized Brazilian patients during the first three years of the coronavirus disease (COVID)-19 pandemic: an epidemiological profile

Abstract

Introduction: In Brazil, few studies were performed regarding the co-detection of respiratory viruses in hospitalized patients. In this way, the study aimed to describe the epidemiological profile of hospitalized patients due to influenza virus infection that presented co-detection with another respiratory virus.

Methods: The epidemiological analysis was made by collecting data from Open-Data-SUS. The study comprised patients infected by the influenza A or B virus with positive co-detection of another respiratory virus, such as adenovirus, bocavirus, metapneumovirus, parainfluenza virus (types 1, 2, 3, and 4), rhinovirus, and respiratory syncytial virus (RSV). The markers [gender, age, clinical signs and symptoms, comorbidities, need for intensive care unit (ICU) treatment, and need for ventilatory support] were associated with the chance of death. The data was collected during the first three years of the coronavirus disease (COVID)-19 pandemic-from December 19, 2019, to April 06, 2023.

Results: A total of 477 patients were included, among them, the influenza A virus was detected in 400 (83.9%) cases. The co-detection occurred, respectively, for RSV (53.0%), rhinovirus (14.0%), adenovirus (13.4%), parainfluenza virus type 1 (10.7%), parainfluenza virus type 3 (5.2%), metapneumovirus (3.8%), parainfluenza virus type 2 (3.6%), bocavirus (3.4%), and parainfluenza virus type 4 (1.5%). The co-detection rate was higher in the male sex (50.7%), age between 0-12 years of age (65.8%), and white individuals (61.8%). The most common clinical symptoms were cough (90.6%), dyspnea (78.8%), and fever (78.6%). A total of 167 (35.0%) people had at least one comorbidity, mainly cardiopathy (14.3%), asthma (8.4%), and diabetes mellitus (7.3%). The need for ICU treatment occurred in 147 (30.8%) cases, with most of them needing ventilatory support (66.8%), mainly non-invasive ones (57.2%). A total of 33 (6.9%) patients died and the main predictors of death were bocavirus infection (OR = 14.78 [95%CI = 2.84-76.98]), metapneumovirus infection (OR = 8.50 [95%CI = 1.86-38.78]), race (other races vs. white people) (OR = 3.67 [95%CI = 1.39-9.74]), cardiopathy (OR = 3.48 [95%CI = 1.13-10.71]), and need for ICU treatment (OR = 7.64 [95%CI = 2.44-23.92]).

Conclusion: Co-detection between the influenza virus and other respiratory viruses occurred, mainly with RSV, rhinovirus, and adenovirus being more common in men, white people, and in the juvenile phase. Co-detection of influenza virus with bocavirus and metapneumovirus was associated with an increased chance of death. Other factors such as race, cardiopathy, and the need for an ICU were also associated with a higher chance of death.

Keywords: adenovirus; bocavirus; influenza virus; metapneumovirus; parainfluenza virus; respiratory syncytial virus; rhinovirus; severe acute respiratory syndrome coronavirus 2.

Figure 1

Epidemiological profile of the viral infection in Brazilian patients hospitalized for severe acute respiratory syndrome caused by the influenza virus. The data were collected in the Open-Data-SUS (https://opendatasus.saude.gov.br/). The data comprised the period from December 19, 2019, to April 06, 2023—three years since the beginning of the coronavirus disease (COVID)-19 pandemic in Brazil. N, number of individuals.

Figure 2

Distribution of respiratory viruses in hospitalized patients due to severe acute respiratory syndrome caused by influenza virus infection according to seasons and date of notification. (A) Number of cases distributed across seasons. (B) Epidemiological profile of the most common viruses according to date. (C) Epidemiological profile of the less common viruses according to date. The data comprised the period from December 19, 2019, to April 06, 2023—three years since the beginning of the coronavirus disease (COVID)-19 pandemic in Brazil. %, percentage; N, number of individuals; RSV, respiratory syncytial virus. Autumn from March 21 to June 21. Winter from June 21 to September 23. Spring from September 23 to December 21. Summer from December 21 to March 21. The dates were presented according to the notification periods.

Figure 3

Distribution of respiratory viruses in hospitalized patients due to severe acute respiratory syndrome caused by influenza virus infection across age groups. The data comprised the period from December 19, 2019, to April 06, 2023—three years since the beginning of the coronavirus disease (COVID)-19 pandemic in Brazil. %, percentage; N, number of individuals; RSV, respiratory syncytial virus.

Figure 4

Epidemiological profile of Brazilian patients hospitalized for severe acute respiratory syndrome caused by the influenza virus who presented co-detection with other respiratory viruses. (A) Distribution of the patients according to the age groups. (B) Distribution of the patients according to the self-declared races. (C) Distribution of the patients according to clinical signs and symptoms. (D) Distribution of the patients according to the comorbidities. (E) Distribution of the patients according to the need for support from the intensive care. (F) Distribution of the patients according to the need for ventilatory support. (G) Distribution of the patients according to the outcome. The data comprised the period from December 19, 2019, to April 06, 2023—three years since the beginning of the coronavirus disease (COVID)-19 pandemic in Brazil. All the data is presented as the number of cases (N) in percentage. ^a: Other clinical signs and symptoms summarize all the clinical signs and symptoms that were not listed previously in the dataset. %, percentage; N, number of individuals; SpO₂, peripheral oxygen saturation.

Figure 5

Association between the epidemiological profile of Brazilian patients hospitalized due to severe acute respiratory syndrome caused by the influenza virus who presented co-detection by other respiratory viruses and the outcomes after hospitalization. The figure presents the results of the bivariate analysis with a significant p-value exclusively to determine the probability of death. ^a: Other races included the patients self-declared as Black people, Asian individuals, Mixed individuals (Pardos), and Indigenous peoples. The data comprised the period from December 19, 2019, to April 06, 2023—three years since the beginning of the coronavirus disease (COVID)-19 pandemic in Brazil. The Chi-square test or Fisher’s exact test was used to estimate the distribution of the clinical and epidemiological markers with respect to outcomes (death or hospital discharge). The alpha error of 0.05 was considered in the bivariate analyses carried out in the study. The data are presented using a log-rank scale. 95%CI, 95% confidence interval; OR, odds ratio.

Figure 6

Multivariable analysis to identify the main predictors of death in Brazilian patients hospitalized due to severe acute respiratory syndrome caused by the influenza virus who presented co-detection by other respiratory viruses. The figure presents the results of the multivariable analysis (markers) with a significant p-value exclusively to determine the chance of death. ^a: Other races included the patients self-declared as Black people, Asian individuals, Mixed individuals (Pardos), and Indigenous peoples. The following markers were included in the model: viral infection (bocavirus, metapneumovirus, rhinovirus, and respiratory syncytial virus), age, race, comorbidities (cardiopathy, hepatic disorder, diabetes mellitus, and immunodepression), need for an intensive care unit, and need for mechanical ventilatory support. The data comprised the period from December 19, 2019, to April 06, 2023—three years since the beginning of the coronavirus disease (COVID)-19 pandemic in Brazil. The multivariable analysis was done using the Binary Logistic Regression model with the Backward Stepwise method. Markers with p ≤ 0.05 in the bivariate analysis were included in the regression model. The dependent variable was the health outcome (death or recovery—hospital discharge). The alpha error of 0.05 was considered in the multivariable analysis carried out in the study. The data are presented using a log-rank scale. 95%CI, 95% confidence interval; OR, odds ratio.